A Genetic Algorithm for the Structural Estimation of Games with Multiple Equilibria

This paper proposes an algorithm to obtain maximum likelihood estimates of structural parameters in discrete games with multiple equilibria. The method combines a genetic algorithm (GA) with a pseudo maximum likelihood (PML) procedure. The GA searches efficiently over the huge space of possible combinations of equilibria in the data. The PML procedure avoids the repeated computation of equilibria for each trial value of the parameters of interest. To test the ability of this method to get maximum likelihood estimates, we present a Monte Carlo experiment in the context of a game of price competition and collusion.